REDUCTION OF STATE VARIABLE DIMENSION IN STOCHASTIC DYNAMIC OPTIMIZATION MODELS WHICH USE TIME-SERIES DATA

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dc.creatorBurt, Oscar R.
dc.creatorTaylor, C. Robert
dc.date2017-04-01T17:48:42Z
dc.date.accessioned2026-07-09T04:16:50Z
dc.descriptionStatistical procedures are developed for reducing the number of autonomous state variables in stochastic dynamic optimization models when these variables follow a stationary process over time. These methods essentially delete part of the information upon which decisions are based while maintaining a logically consistent model. The relatively simple linear autoregressive process as well as the general case is analyzed and the necessary formulae for practical application are derived. Several applications in agricultural economics are discussed and results presented which quantify the relative amount of information sacrificed with the reduction in number of state variables.
dc.identifierdoi:10.22004/ag.econ.32349
dc.identifierhttps://ageconsearch.umn.edu/record/32349/files/14020213.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/32349
dc.identifier.urihttp://hdl.handle.net/123456789/547516
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/32349
dc.titleREDUCTION OF STATE VARIABLE DIMENSION IN STOCHASTIC DYNAMIC OPTIMIZATION MODELS WHICH USE TIME-SERIES DATA
dc.typeText

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